outliers_mahalanobis {Routliers} | R Documentation |
Detecting multivariate outliers using the Mahalanobis distance
outliers_mahalanobis(x, alpha, na.rm)
x |
matrix of bivariate values from which we want to compute outliers |
alpha |
nominal type I error probability (by default .01) |
na.rm |
set whether Missing Values should be excluded (na.rm = TRUE) or not (na.rm = FALSE) - defaults to TRUE |
Returns Call, Max distance, number of outliers
#### Run outliers_mahalanobis data(Attacks) SOC <- rowMeans(Attacks[,c("soc1r","soc2r","soc3r","soc4","soc5","soc6","soc7r", "soc8","soc9","soc10r","soc11","soc12","soc13")]) HSC <- rowMeans(Attacks[,22:46]) res <- outliers_mahalanobis(x = cbind(SOC,HSC), na.rm = TRUE) # A list of elements can be extracted from the function, # such as the position of outliers in the dataset # and the coordinates of outliers res$outliers_pos res$outliers_val